A data-driven framework for integrating visual inspection into injection moulding pipeline
Recent advances in machine vision has led to new opportunities for automating that entire manufacturing pipeline. Consider, for example, the situation where an unattended computer vision system inspects the widget and decides whether or not to discard it. Even this little amount of automation can save many hundreds of person-hours on a typical factory floor. While for simple designs, we now have automated inspection methods relying upon lasers, 3D scanning or other imaging modalities that can decide if a widget has any defect. For complex designs, this ability remains elusive. More importantly, however, automated inspection schemes can only decide if a widget deviates from its intended design, say available in the form of a CAD drawing, it cannot decide what changes should be made down the manufacturing pipeline to prevent similar defects in the future. This project aims to explore machine learning techniques that integrate automated inspection with manufacturing process. Specifically, we will focus on injection moulding process in this project. We will develop new theory and methods for characterizing the injection moulding process in terms of quantities measured via an automated inspection system. This effort will lead to a deeper understanding of the role of myriad of parameters that control injection moulding processes.
Active learning for automatic generation of narratives from numeric financial and supply chain data
Large enterprises compile and analyze large amounts of data on a daily basis. Typically the collected raw data is processed by financial analysts to produce reports. Executive personnel use these reports to oversee the operations and make decisions based on the data. Some of the processing performed by financial analysts can be easily automated by currently available computational tools. These tasks mostly make use of standard transformations on the raw data including visualizations and aggregate summaries. On the other hand automating some of the manual processing requires more involved artificial intelligence techniques.
In our project we aim to solve one of these harder to automate tasks. In fact analyzing textual data using Natural Language Processing (NLP) techniques is one of the standardized methods of data processing in modern software tools. However the vast majority of NLP methods primarily aim to analyze textual data, rather than generate meaningful narratives.
Since the generation of text is a domain-dependent and non-trivial task, the automated generation of narratives requires novel research to be useful in an enterprise environment. In this project we focus on using numerical financial and supply chain data to generate useful textual reports that can be used in the executive level of companies. Upon successful completion of the project, financial analysts will spend less time on repetitive tasks and have more time to focus on reporting tasks requiring higher-level data fusion skills.
Advanced Analytics and Fault Detection of Industrial Steam Traps via Machine Learning
Steam is flexible and very cost-effective in terms of gases that can be used to transfer heat. All steam systems require that the steam arrives at the right quantity, temperature, and pressure for it to work efficiently. The presence of foreign materials such as air, condensate, and other gases can lead to a reduction in this efficiency. To that end, steam traps are used to remove condensate, debris, and other gases from the steam system to maintain operating efficiency and prevent system damage. Due to constant operation, steam traps have a high failure rate of about 20%, according to the US State Department of Energy. Pulse Industrial’s steam trap sensor provides monitoring capabilities and early detection of these steam trap failures. This leads to vast improvements in the operating performance of partner facilities and reduced operating costs. This research project will optimize the analytics and diagnostics performance of currently deployed steam trap monitors. This optimization will be done by developing the AI model using collected data from partner plants. This will allow for a holistic improvement of the overall system, which is expected to increase the accuracy performance of the steam trap sensors
Advancing microarray analysis with GPU-based image analysis
Multiplexed tests detect multiple analytes from a single biological specimen in an automated fashion using microarrays. These can be used to determine patient immune response with minimal invasiveness and to better quantify biomarkers for advanced biological tests. To do this, microarrays require the printing of biological and chemical materials onto an optically transparent substrate (this require dispensing, curing, putting down a protective coating that is then dried to create a plate). Each well contains multiple spots (sensors) to detect different proteins. The proposed work improves analytical tools with a focus on accuracy, significantly decreased time to results and advanced image analysis using capability provided by SOSCIP. The SOSCIP platforms allow testing new approaches using cloud-based analysis as well as develop tools necessary for in-house analysis. The work will enhance the performance of current assay designs and inform the next generation of assays to support the partner’s technology leadership position. The results will be implemented immediately by the industry partner – as has been done in the previous work. The impact will be to place SQI in a unique market position in the diagnostics market. Ultimately, this work aligns with the partner’s goal of “cheaper, better, faster”.
Advancing sustainable aerodynamic solutions with improved modeling
Within current aerospace design, it is necessary to over-engineer features to ensure stability and safety under emergency conditions. It would be ideal to develop capabilities to reduce the size of large elements of commercial aircraft with reliable technologies that ensure safe operation under hazardous conditions. A key advantage of the synthetic jet is that no bulky air source and supply system is required to provide actuation to the flow. The planned changes to the aircraft structure that increase fuel economy and reduce weight will ensure the success of the economically important Canadian aerospace industry.
AI-Powered Virtual Shopping Marketplace Platform for the Hair Integrations Industry
The average wig industry revenue over the last five years has a steady growth to $415.2 million per year. This industry caters to four distinct consumer groups: 1) individuals that purchase wigs for aesthetic purposes, 2) those that have lost their hair due to a medical condition or treatment, 3) those that follow their religious practice for specific hair restrictions, and 4) film/theatre directors who purchase wigs as part of character costumes. A wig costs from $600 to $1500 or more. In addition, with the COVID-19 outbreaks, online shopping inevitably became the leading trends.
However, shopping for a perfect wig online is not an easy task. We will build an AI-powered marketplace to solve the problem. In the AI-powered marketplace, customers get expert advice from AI as if customers are served by domain experts. AI will extract customers’ head shape, skin tone, and personality from the image and video, and make the best recommendations.
An intelligent immersive content creation platform for the non-programmer for training, maintenance and assembly using AR and VR.
OVA’s StellarX is an uncompromisingly good virtual and augmented reality software. Purpose-built for teams that want to collaborate and create in those new paradigms.
Application of advanced machine learning and structure-based approaches for repurposing & discovering therapeutics for COVID-19
The novel COVID-19 pandemic caused by SARS-CoV-2 is a global health emergency of international concern with an estimated death toll in millions worldwide. The development, testing and approval of the COVID-19 vaccine may take at least 12-18 months. By then, the virus could mutate and reduce the vaccine’s efficacy. This project aims to leverage the latest advances in AI to repurpose existing drugs and identify novel ones that could be further developed as COVID-19 therapies in an extremely condensed manner. We will utilize Apollo 1060, 99andBeyond’s AI-augmented decision-making platform that can rapidly search a chemical space that is orders of magnitude larger than competitors. We will collaborate with experimentalists, and aim to have a set of compounds confirmed in a set of in vitro COVID-19 assays within the next six months. The proposed set of compounds and their corresponding biological activity will be openly published to help the community build powerful predictive models for COVID-19 targets. Their further testing and development will require the engagement of collaborating physicians in the hospitals and may attract partnerships with leading pharma and biotech in the US and Canada.
Application of Data Analytics in Industrial CFD
The long-term objective of this project is to develop an efficient interface between the large data sets generated by industrial-level computational fluid dynamics (CFD) simulations and the latest tools that are available for data analytics. Industrial-level CFD is very compute-intensive. This project capitalizes on the experience of the industrial partner (SOTAES) and the CFD group at the University of Windsor, which is currently running large-scale simulations using STAR-CCM+ software on Compute Canada resources for many fundamental studies to understand the evolution of bluff body flow field characteristics.
Applications of Data-Driven Analytics and Decision-Making Optimization for Solar-powered Energy Hubs Integrated with Electrical Vehicles
This project will explore data-based solutions regarding uncertainties and prediction issues associated with the Photovoltaic (PV) systems power output, EV charging load and the buildings’ demand load by utilizing the recent advancements in big data analytics. A data-driven method will be proposed and applied to Boxbrite’s energy systems planning and scheduling framework (e.g., capacity expansion models representing the addition of energy storage batteries, optimal scheduling of building and EV charging loads, minimizing the imported power from the grid etc.). Therefore, high-level optimization tasks such as planning (designing, sizing) and scheduling (operating mode, on-off status) will highly benefit from information mined from massive data, since optimization has always depended on the interchange between models and data. In particular, in this research, a comprehensive framework for microgrids with EV charger systems that can leverage conclusions from big data tools (machine learning) will be developed.
Automated Assessment of Forest Fire Hazards
The proposed project is to develop a Deep Reinforcement Learning (DRL) model capable of using Light Detection and Ranging (LiDAR) data to automatically analyze whether there is a risk of forest fire due to vegetation management around electric equipment. The model automatically classifies points in a LiDAR scan to determine whether there is direct contact between vegetation and electric equipment and outputs control actions for an Unmanned Aerial Vehicle (UAV) that can autonomously inspect infrastructure as needed. Our research will focus on the solution of two different but complementary machine learning problems that the DRL model will have to solve to allow for a UAV to autonomously acquire data with sufficient quality for fire risk assessment. Firstly, there is the problem of perception, i.e., to analyze raw LiDAR data and deduce classifications of individual objects and their shape and orientation. Secondly, there is the problem of control, i.e., to use the perceived state of the environment as determined by the first stage to adequately navigate through the environment until a complete scan has been obtained of the region that we are conducting a fire risk assessment for. Solving these two problems in a harmonious way that allows for an efficient, accurate and a complete scan of the environment is an open research problem in the domain of DRL that we aim to address.
Automatic Identification of Phytoplankton using Deep Learning
Under the impact of global climate changes and human activities, harmful algae blooms (HABs) have become a growing concern due to negative impacts on water related industries, such as safe water supply for fish farmers in aquaculture. The current method of algae identification requires water samples to be sent to a human expert to identify and count all the organisms. Typically this process takes about 1 week, as the water sample must be preserved and then shipped to a lab for analysis. Once at the lab, a human expert must manually look through a microscope, which is both time-consuming and prone to human error. Therefore reliable and cost effective methods of quantifying the type and concentration of algae cells has become critical for ensuring successful water management. Blue Lion Labs, in partnership with the University of Waterloo, is building an innovative system to automatically classify multiple types of algae in-situ and in real-time by using a custom imaging system and deep learning. This will be accomplished using two main steps. First, the technical team will work with an in-house algae expert (a phycologist) to build up a labelled database of images. Second, the research team will design and build a novel neural network architecture to segment and classify the images generated by the imaging system. The result of this proposed research will dramatically reduce the analysis time of a water sample from weeks to hours, and therefore will enable fish farmers to better manage harmful algae blooms.
Big cardiac data
We propose to develop a cloud based image centered informatics system powered by newly developed big data analytics from our group for automatic diagnosis and prognosis of heart failure (HF). HF is a leading cause of morbidity and mortality in Ontario and around the world. There is no cure. Therefore early diagnosis and accurate prediction is very critical before the symptoms appears. However, previously lack of efficient image analytics tool has been the major pitfall to have an accurate prognosis and early diagnosis system. The system will greatly improve the clinical accuracy and enables accurate early diagnosis and prediction by intelligently analyzing all associated historical images and clinical reports. The final system will not only greatly reduce the sudden death and irreversible cardiac conditions, but also offers a great optimization of decision system in current healthcare. This project is based on strength built upon one successful SOSCIP project and two OCE projects.
Big data analytics for the maritime internet of things (IoT)
The Internet of Things (IoT) is an emerging phenomenon that enables ordinary devices to generate sensor data and interact with one another to improve daily life. The maritime world has not escaped to the influence of the IoT revolution. We are in the midst of a technological wave in which vessels are not the only ones carrying sensors (GPS or radar) anymore, but other maritime entities such as cranes, crates, boats, pickup trucks, etc. are being equipped with the same capabilities. This trend constitutes the backbone of the so-called Maritime Internet of Things (mIoT).
This project is about exploiting the tide of sensor data emitted by a myriad of maritime entities in order to improve both internal and collaborative processes of mIoT-related organizations; for instance, think of a Port Authority adjusting its berthing and unloading schedule upon receiving notice that a vessel has been delayed by harsh weather conditions. The challenge addressed by this research project is the generation of actionable intelligence for Decision Support using Big Data analytics. Actionable intelligence includes anomalies, alerts, threats, potential response generation, process refinement and other types of knowledge that improve the efficiency of a maritime-related organization and/or the manner in which it interacts with other similar organizations.
Change Your Game
Tracking athletic performance in basketball can require significant time and resources, but the resulting information can be extremely valuable for a developing player and their coach. Fortunately, advancements in computer vision can provide an opportunity to automatically track shooting performance. Further, this information can be provided to the players in an entertaining and fun gaming atmosphere to encourage player development. The objective of this project is to advance deep learning algorithms to provide real-time biomechanical feedback that can be used to develop training and entertainment software for youth basketball players. This exciting work by Pipeline Studios Ltd., in collaboration with McMaster University, involves the use of deep learning-based approaches to track shooting mechanics and performance. Significant computational resources are initially required to test the limits of these deep learning models for image classification, object detection, event detection, and human pose estimation. The advancement of these algorithms on large computational networks will be required for the training and entertainment software which can support basketball athlete development in Canada.
Quantum Closed-loop Design of Diquats for Use as Electrolytes in Redox Flow Battery Systems
The proposed project aims at a closed-loop discovery of organic redox flow battery electrolytes. In this continuous workflow, properties of organic molecules are predicted using machine learning (ML) models and/or computed using quantum mechanical models, which allows leveraging the costs of the experiments. Then, the lead candidates are synthesized and characterized using automated systems. Finally, the results of characterization are used for adjusting the computational models. We focus on a specific class of organic molecules –diquats– that show high redox reversibility and good chemical stability. A virtual screening pipeline will be developed using proprietary software provided by Kebotix Canada.
Cloud Native Big Data Engineering and Automation
XLScout is a startup engaged in democratizing Innovation and connecting research and development with intellectual property (IP) departments across the world. The company is investing in developing proprietary algorithms, using Artificial Intelligence and Machine learning, to mimic the behaviour of an expert searcher. XLScout focus is on creating a sustainable and adaptive text mining framework that will provide natural Language Processing (NLP)based research outputs for IP in different domains. Various data points have been secured for our solution and they include patent data, litigation data, corporate data, reassignment data, examination data, and other patient-related data. XLScout hosts a data vault of about 130+ million patent documents which occupies approximately 8TB of storage. Document searching, in general ,is a cumbersome process, and specifically, searching the patent-related documents requires advanced strategies that a novice searcher might not be aware of. Therefore, this type of search requires extensive effort and time. The main objective of this project is to automate the patent and non-patent search effectively by allowing machines to understand users’ queries and thus, creating a sustainable and adaptive text mining framework that will provide NLP-based research outputs for IP search in different domains. Furthermore, this project aims to develop scalable solutions for XLScout’s data vault on which the company will run proprietary AI and ML models and generate high-value analytic solutions to help the customers make informed decisions.
Combination of optical imaging and deep learning for better seizure localization to improve epilepsy surgical and Deep Brain Stimulation Therapies
Epilepsy is a debilitating neurological condition defined by recurrent, unprovoked seizures characterized by hypersynchronous neuronal discharge. Although anti-epileptic drugs have shown effectiveness in treating epileptic seizures, approximately 30% of patients are medically refractory and not responsive to them.
This project will use computational strategies based on Neurescence’s proprietary hardware in combination with machine learning to extract a clinically relevant biomarker to support surgical planning, as well as to accurately determine seizure onset time to optimize DBS.